Google's Plan for Autonomous Cars Doesn't Go Far Enough

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Google's Plan for Autonomous Cars Doesn't Go Far Enough

Photo: Audi

Much has been written about the era of connected cars, especially as excitement grows around announcements that besides Google, Audi, Nissan, Tesla, Mercedes Benz, and others are planning to make commercially available self-driving cars, too.

The discussions range from the ethics of autonomous cars to every latest announcement around the technology involved in Google’s own self-driving car project – from wearables to manufacturing. But there’s a danger to these one-dimensional discussions: We can't rely on the technology *inside *the car alone.

We need to think about what’s outside, too – a smart, interconnect infrastructure for our roadways.

It’s moving from thinking only about traffic lights, signs, and crosswalk lights to adding intelligence into pavement, utilities, and the like. This will require changes in how we think about business models, job functions, and more. Because our existing roadways aren’t inert objects: They’re dynamic systems comprised of the interplay between cars and traffic signals, as well as repaving and restriping.

With autonomous cars, infrastructure enters the realm of science fiction. Imagine charging electric vehicles as they drive over the roads (this has already been prototyped in Korea). Or changing pavement color to warn drivers when the temperature drops below freezing, or other hazards are ahead.

#### Terry Bennett

##### About

Certified LS LPF MRICS LEED AP, Terry Bennett is on the Sustainable Infrastructure Advisory Board at Harvard's Graduate School of Design and leads industry strategy for infrastructure at Autodesk. Bennett is also a founding editorial board member of Rebuilding America’s Infrastructure Magazine; member of the Urban Land Institute’s Public Development & Infrastructure Council; and Charter Member and Economics Council Member of the Institute of Sustainable Infrastructure.

Infrastructure may sound boring but it’s actually an interesting challenge, especially as rapid technology advances force governments – both local and broader – to decide what future needs to accommodate. When it comes to “autonomous”, “robotic”, or “self-driving” cars (of the ilk Google has popularized) and other such transport (like autonomous buses), intelligent controls need to migrate from the physical ground infrastructure to a distributed embedded one.

For traffic congestion, rethinking infrastructure for autonomous cars means going beyond the simple brute force approach of adding lanes to highways while ignoring the other realities of moving people in the 21st century. Study after study shows that building new roads alone does not solve congestion problems. New highways often just attract more traffic. American drivers log about twice as many miles (1.4 trillion per year) as they did say 30 years ago on much of the same roads – with only single digit percent increases in new roads. Traffic congestion keeps rising, with nearly 4 billion hours of driver delays a year (not to mention over 2 billion gallons of wasted fuel).

But these efforts are an uncoordinated mishmash of relatively unsophisticated data-gathering methods haphazardly connected to infrastructure – not a coherent and intelligent transportation system where all the parts work together purposefully. This matters, because ultimately, the smartest vehicles are only as smart as the infrastructure that surrounds them.

So we need to focus more on thinking about advances in infrastructure along with advances in the components, interfaces, and related devices.

We need to design a system where cars can talk to the road, other cars, or a transportation management center. We already have the technology we need to do this: GPS; Wi-Fi; embedded sensors; 3-D planning, design, and construction tools. But here’s what else we need to do to make autonomous cars work at scale.

Break down the infrastructure industry’s traditional silos between people, applications, and workflows. By rethinking holistically how we plan, design, and build integrated transportation systems, we can ensure data isn’t just collected, but used to get rid of slow traffic lights. For example, by favoring automated turns at intersections; using self-governing engines to synchronize speed and merging; or providing real-time feedback for crowdsourced projects like StreetBump. All of this paves the way for “infrastructure modularity” – the industry term-of-art for the ability to change and adapt to new innovations and new modes of transport over time.

Create ways for cars to collect, coordinate, and upload roadway info, so the physical environment can “listen” to roadway sensors and optimize traffic performance in real-time, as well as “learn” and adjust longer-term patterns. By leveraging and installing wireless transponders called Roadside Units or similar smart embedded sensors, cars can feed safety information into our highways and rural roads. Such information would include static road hazards like curvy roads or low bridges; changing risks such as construction; and information about traffic density, flow, volume and speed. It’s not unlike transferring data back and forth to high-speed tolling lanes – but with much richer data.

__Designate transportation ____mega regions __delineated by need versus political jurisdictions. Just as Eisenhower did to create highway systems in the first place, we need to eliminate state lines – at least when it comes to transportation planning, designing, and funding. Carmakers and the U.S. Department of Transportation (as well as its 50 state agencies) need to work together to create a national “smart highway” initiative. It’s the only way we can draft system-level plans, rather than having the patchwork of competing plans within neighboring towns or states like we have now. After all, funding a smart system in one state and not in a neighboring one defeats the purpose of such a system in the first place.

__Encourage more public-private joint ventures __so local, national, and international governments can keep pace with smarter transportation innovations. According to McKinsey Global Institute’s report on infrastructure productivity, there’s an estimated $57 trillion infrastructure funding needed worldwide. One way to kickstart these funds and help all stakeholders better understand the cost, traffic performance, and environmental impact is to explore “what if” scenarios using 3-D models, as well as different design and build options. It’s the only way both public and private investors can truly understand the scope, complexity, and lifecycle cost of an infrastructure project. With better models and therefore better understanding, we are less prone to the massive cost overruns and irate taxpayers that have become synonymous with large transportation endeavors.

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What Google has started is a huge advance – and a healthy prod to the automotive industry. But it can’t be just the car that is smart; the car also needs to play a role as a real-time data contributor to the overall transportation network, too. This is where the intelligence of an autonomous system really shines. It will also set the stage for a day when the cars of today or even the autonomous cars of tomorrow are replaced by the desired modular approach to infrastructure.

Whether at a community, metropolitan, or even national level, smarter infrastructure will not only change transportation – but provide the foundation for smart cities where other kinds of infrastructure (roads, water, electricity) all talk to each other. This kind of vision goes beyond hyperloop dreams to the reality of solving problems for people everyday.